Technical Field
The present disclosure relates to an image processing apparatus and method. More particularly, the present disclosure relates to an image processing apparatus and a method for increasing sharpness of images.
Description of Related Art
With restrictions posed by the transmission rate and storage capacity, videos and images are often transmitted or stored in low-resolution formats. Image interpolation is frequently used to increase the resolution of the videos and images, such that they can be displayed on high-resolution display devices. However, although the number of pixels is increased with image interpolation techniques, the generated images and videos are often blurry and of low image quality. Super resolution (SR) technology is proposed to add details to the blurry images, and there are various techniques to realize super resolution.
The first technique is to construct a single frame of high-resolution image using multiple frames of low-resolution images. The low-resolution images taken during a short time segment cover the same scene, and thus include different details of the scene. This technique extracts details from the low-resolution images taken at approximately the same time and combines the details to form a high-resolution image. However, this technique consumes significantly more memory space than the memory space needed for the low-resolution images.
Another technique is generating high-resolution images using a learning-based algorithm. This technique generates high-resolution images by comparing features of image blocks in a low-resolution image with data stored in a high-resolution image database, and replacing the image blocks of the low-resolution image with high-resolution image blocks which match the features from the high-resolution image database. However, this technique requires large memory space for the high-resolution image database and is computationally intensive when searching the high-resolution image database with the features of the image blocks.